SOTA semantic segmentation algorithms use deep neural network architectures to exploit large annotated datasets [1]}, [2]}, [3]}, [4]}. These approaches are based on training a CNN encoder using manually annotated segmentation maps to learn a latent embedding of the data. An up-sampling decoder combined with a classifier is then used to infer pixel-wise estimations for the true semantic labels. Performance of such methods is high when large amounts of annotated data are available for supervised training. However, these methods are not suitable when the goal is to transfer knowledge between different domains [5]}, [6]}.
Model adaptation to target domains has been explored in both semi-supervised and unsupervised settings. Semi-supervised approaches rely on the presence of a small number of annotated target data samples [7]}, [8]}. For example, a weakly supervised signal on the target domain can be obtained using bounding boxes. However, manual data annotation of a small number of images is still a considerable bottleneck in the area of medical imaging because only trained professionals can perform this task. For this reason, UDA algorithms are more appealing for healthcare applications.
